no code implementations • ECCV 2020 • Yanchun Xie, Jimin Xiao, Ming-Jie Sun, Chao Yao, Kai-Zhu Huang
To this end, we engaged neural texture transfer to swap texture features between the low-resolution image and the high-resolution reference image.
no code implementations • 29 Feb 2024 • Juexiao Feng, Yuhong Yang, Yanchun Xie, Yaqian Li, Yandong Guo, Yuchen Guo, Yuwei He, Liuyu Xiang, Guiguang Ding
In recent years, object detection in deep learning has experienced rapid development.
1 code implementation • 9 Nov 2023 • Jinjin Xu, Liwu Xu, Yuzhe Yang, Xiang Li, Fanyi Wang, Yanchun Xie, Yi-Jie Huang, Yaqian Li
Recent advancements in multi-modal large language models (MLLMs) have led to substantial improvements in visual understanding, primarily driven by sophisticated modality alignment strategies.
2 code implementations • 23 Oct 2023 • Xinyu Huang, Yi-Jie Huang, Youcai Zhang, Weiwei Tian, Rui Feng, Yuejie Zhang, Yanchun Xie, Yaqian Li, Lei Zhang
Specifically, for predefined commonly used tag categories, RAM++ showcases 10. 2 mAP and 15. 4 mAP enhancements over CLIP on OpenImages and ImageNet.
no code implementations • 28 Jul 2023 • Liwu Xu, Jinjin Xu, Yuzhe Yang, YiJie Huang, Yanchun Xie, Yaqian Li
Specifically, we first integrate and leverage a multi-source unlabeled dataset to align rich features between a given visual encoder and an off-the-shelf CLIP image encoder via feature alignment loss.
2 code implementations • 6 Jun 2023 • Youcai Zhang, Xinyu Huang, Jinyu Ma, Zhaoyang Li, Zhaochuan Luo, Yanchun Xie, Yuzhuo Qin, Tong Luo, Yaqian Li, Shilong Liu, Yandong Guo, Lei Zhang
We are releasing the RAM at \url{https://recognize-anything. github. io/} to foster the advancements of large models in computer vision.
no code implementations • 8 Nov 2018 • Yanchun Xie, Jimin Xiao, Kai-Zhu Huang, Jeyarajan Thiyagalingam, Yao Zhao
In this paper, we propose a novel approach to address the correlation filter update problem.
no code implementations • 16 May 2017 • Jimin Xiao, Yanchun Xie, Tammam Tillo, Kai-Zhu Huang, Yunchao Wei, Jiashi Feng
In addition, to relieve the negative effect caused by varying visual appearances of the same individual, IAN introduces a novel center loss that can increase the intra-class compactness of feature representations.